Abstract : A measure of effectiveness of a statistical pattern recognition process is suggested and applied to the problem of feature selection. The classification process is one which approximates the class populations by probability distributions in the feature space based on samples of prototype patterns. It uses a Bayes classifier. The suggested measure of effectiveness is a normalized estimate of mutual information obtained directly from the prototypes. The feature selection procedure chooses likely feature subsets and tests them against thresholds related to the effectiveness of the full set of features. The subsets are chosen by ordering the original features according to individual effectiveness and then alternately truncating and permuting the remaining space until a minimum is reached. The minimum is quasioptimum in the sense that it contains no useless features. (Author)